Google Antigravity revolutionizes software development as an agentic IDE powered by Gemini 3 Pro, enabling AI agents to autonomously plan, code, test, and debug across editors, terminals, and browsers. Launched in November 2025, it shifts from mere code completion to full task orchestration, supporting free access to top models like Claude Sonnet 4.5. Developers praise its agent manager for parallel workflows and browser automation, though some note occasional errors in complex scenarios.
User reviews highlight Antigravity’s strengths in vibe coding—rapid, intuitive development—often comparing it favorably to Cursor for autonomous task handling. One developer built a 50k-line project using it alongside other tools, calling its IDE interface clear for step-by-step guidance. Others appreciate the plan mode for precise execution in production-like use cases, like Gmail extensions, with seamless model switching when limits hit. While early bugs frustrated some, its verifiable “artifacts” and browser agent for visual debugging earn high marks for transparency.
Project Origins in Python Vibe Coding Translation to Java with Cursor
This journey began with a Python-based MCP system, a modular setup handling document processing, OCR, Qdrant vector storage, and web serving.
Pure vibe coding via Cursor transformed the Python codebase into Java, leveraging AI for swift refactoring without deep manual intervention. The result: a robust Java equivalent ready for cross-platform deployment
Native Linux Execution
The Java port ran flawlessly on a native Linux machine, validating the translation through successful compilation of modules like mcpfiles, mcpocr, mcpdoc, and mcpqdrant. Startup scripts executed cleanly, confirming arithmetic logic and process management. We then wanted to bring this application into a contanierized Java instance.
A Java 21 container was chosen for its long-term support and compatibility with modern Gradle builds. This base image provided the runtime essentials, setting up for dependency management with an existing .m2 folder import to bypass redownloads. The decision optimized for performance in containerized microservices like the MCP mesh
Docker Desktop on Windows Import
Importing to a Windows machine via Docker Desktop initially introduced environment mismatches, such as missing Unix utilities. Docker ensured isolation, but initial runs exposed network binding issues—servers stuck to localhost instead of 0.0.0.0—and premature shutdowns from faulty trap logic. Antigravity’s intervention turned this into a success story.
Antigravity’s Dockerfile and health check Enhancements
Antigravity analyzed the debugging summary and suggested precise Dockerfile additions: installing bc for arithmetic in startup scripts and iproute2 for troubleshoot commands. These fixes addressed command not found errors and network diagnostics, making the container self-sufficient. The agent’s suggestions were verifiable artifacts, showcasing its terminal-aware reasoning.
Further tweaks from Antigravity adapted configs for Docker realities: binding MCP servers to 0.0.0.0 for external access and refining start script for proper signal handling. It corrected shell syntax like unary operator errors from missing bc, preventing runtime crashes. These changes ensured mesh services communicated seamlessly.
Antigravity upgraded health checks with robust wait logic, verifying servers reached “READY” status before deeming startup complete. Build failure detection was added, halting runs if JARs were absent. This proactive approach minimized downtime in container restarts.
Autonomous Browser Debugging
Antigravity autonomously operated browsers for debugging, inspecting frontend issues like empty chat areas. This visual agent capability accelerated fixes beyond terminal limits.
It was a bit scary seeing the browser open itself and typing autonomously on the GUI in order to test GUI capabilities, however this really empowers the debugging and makes the tool fully autonomous in troubleshooting
Terminal Command Corrections in Docker
Initial terminal commands failed as Antigravity overlooked the Docker context, suggesting first native Linux syntax, then powershell ones. After iterative prompts, it adapted—replacing direct paths with container mounts and docker exec equivalents (adding a docker run in fron of every command). This learning curve underscored agent adaptability, culminating in flawless executions.
Antigravity elevated this project from Python prototype to production-ready Dockerized Java app, blending vibe coding with agentic precision. Cross-platform hurdles became showcases of AI’s evolving role in DevOps, proving tools like this can handle real-world messiness autonomously. For indie builders, it’s a game-changer—faster iterations, fewer errors, endless potential.
VibeOps 
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